Spoken Arabic Digits Recognition System Using Convolutional Neural Network

Abstract

Digit recognition has a vital use in multiple human-machine interaction applications. It is used in telephone-based services, such as dialing systems, airline reservation systems, different bank transactions, and price extraction. This research aims to develop a new Convolution Neural Network (CNN) based spoken digits recognition system for the Arabic digits. The developed system used a classification approach to perform the recognition task. First, the Mel frequency cepstral coefficients of the spoken digits were conducted and reduced in the convolution phase. Then in the classification phase, the most appropriate digit label for the testing utterances is produced. The proposed approach has shown a remarkable performance when compared to similar systems. The recognition system achieved a 99% correct digit recognition compared to 98% using Recurrent Neural Networks based digit recognition system.